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Raw Mix Control- Modul: PrepMaster Analytics

Advanced control system using adaptive material modeling, multi-stage optimization, and robust process handling for stable, efficient cement production.

Mill process control dashboard with real-time overview of throughput, setpoints, and belt weigher loading: shows current production rate (239.9 t/h vs. target 240 t/h), automatic operation mode, recipe and analysis values (Fe2O3, LSF, SR), as well as material composition and dosing of individual feeders in percent. Real-time overview of throughput, setpoints, and feeder loading status
Graphical representation of oxide development over time for Fe2O3, SiO2, Al2O3, and CaO: shows measured values and forecasts in comparison, providing a real-time overview of fluctuations for monitoring and optimizing the blending process. Graphic display of course of oxides
Graphical representation of oxide estimations generated by the AMT function over time for Loss, SiO2, Al2O3, Fe2O3, and CaO: shows calculated values compared to reference and limit values for monitoring and optimizing the blending process. Graphic display of oxides estimation by the AMT function
Graphical representation of detailed trends of belt weigher performance, setpoints, and average values for individual feeders: shows the time-based development of feed rates, deviations from setpoints, and current as well as calculated values for analyzing and optimizing the blending process. Detailed trends of actual, target, and average feeder rates
Graphical representation of combined trends of belt weigher performance with oxide and KPI values over time: shows feed rates of different materials, setpoints, and their relationship with quality indicators for monitoring and optimizing the blending process. Visualization of production phases with corresponding material feed rates
Graphical representation comparing actual, setpoint, and calculated KPI and oxide values over time: shows deviations and trends for indicators such as SR, LSF, and Fe2O3 to evaluate and optimize process quality. Comparison of actual, target, and calculated KPI and oxide values
Overview for flexible configuration of belt weigher setup, constraints, and system parameters: shows settings for individual feeders such as material assignment, inclusion in analysis and setpoints, minimum and maximum feed rates, delays, and limit and warning values. Flexible configuration of feeder setup, constraints, and system parameters
Graphical representation of the impact of optimization on material costs and CO₂ emissions over time: shows cost development, cost distribution by materials, and calculated CO₂ impact to evaluate efficiency and sustainability of the blending process. Impact of optimization on material cost and CO₂ emissions
Overview for defining products, target values, tolerances, and KPI priorities: shows recipes with material proportions, specifications with limits, and weightings to control and optimize product quality. Recipe Configuration: Definition of products, targets, tolerances, and KPI priorities
Production scheduling overview with controlled transitions between recipes considering belt weigher dynamics: shows time-based product changes, associated recipes, and automatic adjustments to ensure a stable blending process. Changeover Management: Controlled transitions between recipes considering feeder dynamics
Mill process control dashboard with real-time overview of throughput, setpoints, and belt weigher loading: shows current production rate (239.9 t/h vs. target 240 t/h), automatic operation mode, recipe and analysis values (Fe2O3, LSF, SR), as well as material composition and dosing of individual feeders in percent.
Graphical representation of oxide development over time for Fe2O3, SiO2, Al2O3, and CaO: shows measured values and forecasts in comparison, providing a real-time overview of fluctuations for monitoring and optimizing the blending process.
Graphical representation of oxide estimations generated by the AMT function over time for Loss, SiO2, Al2O3, Fe2O3, and CaO: shows calculated values compared to reference and limit values for monitoring and optimizing the blending process.
Graphical representation of detailed trends of belt weigher performance, setpoints, and average values for individual feeders: shows the time-based development of feed rates, deviations from setpoints, and current as well as calculated values for analyzing and optimizing the blending process.
Graphical representation of combined trends of belt weigher performance with oxide and KPI values over time: shows feed rates of different materials, setpoints, and their relationship with quality indicators for monitoring and optimizing the blending process.
Graphical representation comparing actual, setpoint, and calculated KPI and oxide values over time: shows deviations and trends for indicators such as SR, LSF, and Fe2O3 to evaluate and optimize process quality.
Overview for flexible configuration of belt weigher setup, constraints, and system parameters: shows settings for individual feeders such as material assignment, inclusion in analysis and setpoints, minimum and maximum feed rates, delays, and limit and warning values.
Graphical representation of the impact of optimization on material costs and CO₂ emissions over time: shows cost development, cost distribution by materials, and calculated CO₂ impact to evaluate efficiency and sustainability of the blending process.
Overview for defining products, target values, tolerances, and KPI priorities: shows recipes with material proportions, specifications with limits, and weightings to control and optimize product quality.
Production scheduling overview with controlled transitions between recipes considering belt weigher dynamics: shows time-based product changes, associated recipes, and automatic adjustments to ensure a stable blending process.

All advantages at a glance

Adaptive Material Modeling (AMT)

The Raw Mix Control Module applies Adaptive Material Tracking (AMT) to continuously estimate the chemical composition of all raw materials. Analytical data from online systems (CBA) and laboratory instruments (XRF, XRD) are combined with real-time feeder rates to update oxide vectors dynamically. Data quality is ensured through active and passive filtering of non-representative measurements caused by sampling effects or material inhomogeneities. Reference analyses (e.g., composite samples) can be incorporated to stabilize the model. This results in a continuously updated, statistically robust representation of material properties for downstream control calculations.

Multi-Stage Setpoint Calculation

Setpoints for all weigh feeders are calculated based on the current material model and defined quality targets (e.g., LSF, SR, AR). The calculation follows a structured multi-stage approach. First, a technically feasible solution is determined under all operational constraints. If no valid solution exists, target tolerances are systematically relaxed within predefined limits. Once feasibility is ensured, a second optimization step minimizes cost and CO₂ emissions. This staged methodology guarantees both solution robustness and economic efficiency while maintaining compliance with process constraints.

Dynamic Process Stabilization and Disturbance Handling

The control system incorporates mechanisms for detecting and handling process disturbances such as feeder starvation, material inconsistencies, or deviations between predicted and measured values. In such cases, adaptive correction strategies are applied. Additionally, model reset functions allow recalibration to a defined reference state when persistent deviations occur. Setpoint changes are applied incrementally to account for system dynamics and feeder inertia. This ensures stable process transitions, minimizes oscillations, and maintains kiln stability under varying operating conditions.
 

Silo Composition Integration

The module supports the inclusion of silo composition in the control model to improve prediction accuracy. Silo content can be estimated internally based on historical material flows and analytical data or provided by external silo control systems via standardized interfaces (e.g., JSON). This information is incorporated into the setpoint calculation, enabling compensation of accumulated deviations and more accurate control of the effective raw mill feed. The approach is particularly beneficial in systems with significant buffering and time-delayed material transport.

Flexible KPI Framework and Recipe Control

In addition to standard quality parameters, the system supports user-defined linear KPIs derived from oxide compositions. These include pseudo-components such as Bogue phases (C3S, C2S, C3A), enabling closer linkage between raw mix control and clinker formation behavior. Recipes define product targets, material constraints, and optimization priorities, including cost and CO₂ weighting. Materials can be configured with full property sets (chemistry, moisture, LOI, cost) and are automatically integrated into the model. Performance monitoring allows evaluation and continuous optimization of recipe effectiveness.

Process Visualization and Analytical Tools

The system provides structured visualization of process data through dedicated dashboards and analysis views. These include real-time overviews of feeder operation, historical trends of process variables, and comparisons between measured, estimated, and predicted values. Additional views support analysis of KPI compliance, material composition, and setpoint deviations. Custom dashboards and reporting tools enable deeper data analysis, including anomaly detection and validation of material behavior. This ensures full traceability of control decisions and supports data-driven process optimization.

Downloads:

Application Note:
Efficiency of PrepMaster Analytics Rawmix Control with PGNAA Cross-Belt Analysis

Language: english (754 KB)

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